{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Human Activity Recognition (HAR) explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import os\n",
    "from utils.utilities import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train, labels_train, list_ch_train = read_data(data_path=\"./data/\", split=\"train\") # train\n",
    "X_test, labels_test, list_ch_test = read_data(data_path=\"./data/\", split=\"test\") # test\n",
    "\n",
    "assert list_ch_train == list_ch_test, \"Mistmatch in channels!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape: N = 7352, steps = 128, channels = 9\n",
      "Test data shape: N = 2947, steps = 128, channels = 9\n"
     ]
    }
   ],
   "source": [
    "print (\"Training data shape: N = {:d}, steps = {:d}, channels = {:d}\".format(X_train.shape[0],\n",
    "                                                                             X_train.shape[1],\n",
    "                                                                             X_train.shape[2]))\n",
    "print (\"Test data shape: N = {:d}, steps = {:d}, channels = {:d}\".format(X_test.shape[0],\n",
    "                                                                         X_test.shape[1],\n",
    "                                                                         X_test.shape[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Mean value for each channel at each step\n",
    "all_data = np.concatenate((X_train,X_test), axis = 0)\n",
    "means_ = np.zeros((all_data.shape[1],all_data.shape[2]))\n",
    "stds_ = np.zeros((all_data.shape[1],all_data.shape[2]))\n",
    "\n",
    "for ch in range(X_train.shape[2]):\n",
    "    means_[:,ch] = np.mean(all_data[:,:,ch], axis=0)\n",
    "    stds_[:,ch] = np.std(all_data[:,:,ch], axis=0)\n",
    "    \n",
    "df_mean = pd.DataFrame(data = means_)\n",
    "df_std = pd.DataFrame(data = stds_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GeCBeOFISdY2JJCZPy3sRcJVuJRnU4XS4qK6xdkZGhjt2nfWrS6oq2p0txXhMl9RLV8nb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\neYuIJOj/A+ClBY0V0L4GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa19828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_std.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GeCBeOFISdY2JJCZPy3sRcJVuJRnU4XS4qK6xdkZGhjt2nfWrS6oq2p0txXhMl9RLV8nb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\neYuIJOj/A+ClBY0V0L4GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fd0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_std.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Some channels have mean values near 1, most close to 0. Let's standardize them all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test = standardize(X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Check Mean value for each channel at each step\n",
    "all_data = np.concatenate((X_train,X_test), axis = 0)\n",
    "means_ = np.zeros((all_data.shape[1],all_data.shape[2]))\n",
    "stds_ = np.zeros((all_data.shape[1],all_data.shape[2]))\n",
    "\n",
    "for ch in range(X_train.shape[2]):\n",
    "    means_[:,ch] = np.mean(all_data[:,:,ch], axis=0)\n",
    "    stds_[:,ch] = np.std(all_data[:,:,ch], axis=0)\n",
    "    \n",
    "df_mean = pd.DataFrame(data = means_)\n",
    "df_std = pd.DataFrame(data = stds_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J2VjZoWRfwVYON0QbJklLgVuA8yNiQ775bkkr8p+PAT5fR2xWDue4nYqGTYY+HjqoJoxD\n9mpYsZb0uhcCE8AaSWvybecAl0naA/gqT/wfsDQ5xy00b/Ee9njooFIbcxtGrEWfQR/joeeQNeTZ\nDh8sMmsa57idCifp5GNltwEfjohrgc7xbY+VmZnVYN7i7bEyM7NmKhrz9liZmVkDFY15e6zMzKyB\nPEnHrIDvlmRN5FUFzcwS5OJtZpYgD5tUoI0LwZtZvdzzNjNLkIu3mVmCXLzNzBLk4m1mliAXbzOz\nBPlqEzMbeSlOxGp18fbNac2srVpdvC1tKR18fS2/Vc1j3mZmCXLxNjNLkIdNbGg8lPCEFE+IWbO5\n521mliD3vM0awt9UrB8u3mY2kJSuBmojF+8GKKMRuFdmNlpcvK027rmZDW6g4i3pacB64MXAT4Ez\nIuIbZQZm9XKO2885TtugPe/jgL0i4qWSDgbWASv7eQH3uhpvwTm2xnOO+1BFzepn+HNsenq67x1I\nugT4QkR8LH/83Yh4Tt8vZI3lHLefc5y2Qa/z3ht4sOPxY5I8ft4uznH7OccJG7R4PwSMd75OROws\nIR5rDue4/ZzjhA1avLcArwLIx8q+UlpE1hTOcfs5xwkb9CvSRuBISVuBMeDU8kKyhnCO2885TthA\nJyxHgaRfAd4DLAEeA94QEXfVG5WVRdIpwLkdm5YAvwD8QkT8oJ6orGySjgf+Engc2E52OeT99UZV\nDi9M1YWkxcAtwMURcSDw18BH643KyhQR10TEARFxAPDrwPeBs12420PS04GPACfkeb4JuKzeqMrj\nM8vdHQXcHxGfzh/fBHyrxnhsuM4HfhgRV9QdiJVqN7LhoCX542cC/1dfOOVKunjnX4leExEnlvzS\nzwe+L+lqstlnDwDnNWlGmqSDgHdGxIo69t+PIeZpYB25XA4cABxZb0Rza2Kum5TTudplRDws6Y3A\nVknbyIr5b9UUY+k5THbYRNKlwNsZznvYnews/Aci4iVkY9+fBn6XfEYacAHZjLTKSToPuArYq479\n92PIeVqI48g+v41kQ2RvqTec7pqY6wbmdNdMUTraZX7e6s+AF0bEs4G/AW6QNFZlcMPKYVM+/EFs\nBc4c0mt/D/jPiPhXgIi4keyo/SrgM/m2O4GXDGn/Re4HTqhp3/0aZp4W4hCyXL4WuJj6clmkiblu\nWk5ncjm7Xb4S2NJxgvJ9wIuAZ1Uc31By2PhhE0mn89Re0akR8XFJK4a0203AOknLI+IuSYcB0/m/\np8xIq3piQ0TcIGlZlfssUlOeFmJvsquInkdWjGrJZZE6c51QTueaKfpvwNmSluYnoo8DvhURP6oy\nuGHlsPHFOyKuBq6ueJ/fl3QcsF7SM8jG0U7I/3lGWhd15GmBHiIr3P8TEY9Kci5nSSinc80U/Zyk\ndwGbJf0M+F9atPBW44t3XSLin4GDOrdJWgocC3zCM9KStwU4NiKe51wmbwtztMuIeB/ZcEnruHj3\nxzPS2sO5bI+RzKVnWJqZJSjlq03MzEaWi7eZWYIqGfOemtqxa2xmYmIx27c/UsVuS5FSvJ2xTk6O\nVzoRoTPH82nS55l6LHXkuEmfWb9SjH2+HFfe8160aLeqd7kgKcWbQqxNitGx9C+VOLtJOfZuPGxi\nZpagVl8qWMbdnm9e15pr+kdSL/8H+rljt1WvKIejmj/3vM3MEtTqnreZtd+ofrtyz9vMLEEu3mZm\nCfKwiY08nxBrvzbm2D1vM7MEuXibmSXIxdvMLEEu3mZmCXLxNjNL0LxXm0jaHdgALAP2BC4C7gM+\nSHYz3nuBsyLi8aFGWaNjV9047+9TPEtto8XtuJ2Ket4nAdsi4lDgaOC9wCXA6nzbGC26oadZS7kd\nt1DRdd7XAdfnP48BO4HlwO35tk3AUWT3kLMEuVdWrAXTr92OW2je4h0RDwNIGidL/mpgbUTMLLy/\nA1hStJOJicVPWkt3cnJ80Hgbp5eGXeXKhAN8tjO9spMl7QPck/9bHRGbJV1O1itzw05Ume0Y2tV+\nZ6T4ngpnWEraj6zhro+IayVd3PHrceCBotfovHvF5OQ4U1M7Bgg1XVW9387Pto//jKX0ymYfoIvi\nbJsy3tMwP5ey2nFb229T39N8/yeKTlguBW4Bzo6IW/PNd0taERGbgWOA20qK02pQVq+s19tLufF3\nN8jn0muxdztup6Ke94XABLBG0pp82znAZZL2AL7KE702S1QZvTJrNLfjFioa8z6HLMmzHT6ccKxq\n7pW1n9txO3lVQUu6V1bGre7MUuTiPeLcKzNLk6fHm5klyMXbzCxBLt5mZgly8TYzS5BPWJrZyEtx\n/Rr3vM3MEuTibWaWIBdvM7MEuXibmSXIxdvMLEEu3mZmCXLxNjNLUE/XeUs6CHhnRKyQ9Dx8f0Oz\n5DSxHXtVyMH1chu084CTgR/nm2buOu37G7ZIExt2SoqKUN0TPNyO26eXYZP7gRM6Hs++v+ERZQdl\n1cob9lXAXvmmmYZ9KNl9Lau7g7INi9txyxT2vCPiBknLOjaNNeXu8ceuurGU1xm2Km+4O+C+Zhr2\nh/PHvgFxyXp5z8P8XMpqxzCa+YPmve9B1jbp/Prsu8f3oOF3jy+lYY/6DYiLFL3nYd6AeA4DteNR\nzR/Uc4f5ge8ePwff37BkDVwUp++GbclxOy5Z1e14kOK9Crgyhfsb2sAqadhNP8nXcm7HieupeEfE\nt4GD85+/hu9v2HZu2C3kdtwuXs/bADdss9S4eJvZ0HgSzvB4eryZWYJcvM3MEuRhE2ssf+U2m5t7\n3mZmCaqt5+1elY2SBk7Esj41rWa5521mliCPeVegaUfsqozq+x4Vzm+93PM2M0uQi7eZWYJcvM3M\nEuQx70R4BT6z9JXZjl28zRrCB2jrx0DFW9LTgPXAi4GfAmdExDfKDMzq5Ry3n3OctkHHvI8D9oqI\nlwIXAOvKC8kawjluP+c4YYMW70OAzwBExJ3AS0qLyJrCOW4/5zhhg4557w082PH4MUmLImJntz+e\nnBwfm/WYm9etHHDXNp8S73C9oBwDznHzDZTjmf9jzm+9Bu15P0R2Y9pdrzNXwi1ZznH7OccJG7R4\nbwFeBSDpYOArpUVkTeEct59znLBBh002AkdK2gqMAaeWF5I1hHPcfs5xwsamp6frjsHMzPrk6fFm\nZgly8TYzS5CLt5lZgmpb20TS8cBrIuLEumKYS4rThiUdBLwzIlbUHctcJC0BPkJ2ffEewLkR8S8V\nx9CY3EraHdgALAP2BC6KiJvqiKVfTW6/nZqU77LV0vOWdCnw9rr234Okpg1LOg+4Ctir7lgKnAvc\nGhGHA68H3ldDDE3K7UnAtog4FDgaeG+NsfQsgfbbqUn5LlVdH/5W4Mya9t2L1KYN3w+cUHcQPfg7\n4Ir850XA/9UQQ5Nyex2wJv95DEhlgkzT22+nJuW7VEMdNpF0OvCWWZtPjYiPS1oxzH0vUF/ThusW\nETdIWlZ3HJ3myf0XJf0c2fDJm6uPrDm5jYiHASSNA9cDq6uOYT4Jt99Ojcl32YZavCPiauDqYe5j\nSDxteIHmyr2kXwE+Brw1Im6vPLCG5VbSfmSTZdZHxLV1xdFNwu23U6PyXaYUxqzq4GnDQyDphWRD\nBSdGxKaawmhMbiUtBW4Bzo+IDXXF0XKNyXfZfCed7jxteDjeTnZS9VJJAA9GRNVL0zUptxcCE8Aa\nSTNj38dExE9qjKltmpTvUnl6vJlZgjxsYmaWIBdvM7MEuXibmSXIxdvMLEEu3hWTdJCkzWU/X9KJ\nkipdJ8S6KzvHkvaVdKOkf5a0RdIvlhGnDW4IOT5Q0nclbc7/vbboNXypYIXyNUhOBn5c5vMlHQic\nTnYplNVoSDm+GPhoRHxC0suAXyJbEsFqMKQcLwcuiYie115x8a7WzBokH4Zdsw0vIyu624DTIuLB\nuZ/+5Ofnr/Es4G/JpppfOZywrQ+l5xj4LeDLkv4J+DZwTvlhWx+GkePl2UtpJfB14M0RsWO+IDxs\nUqGIuAF4tGPTlcBZ+TKunwbOk3S0pHtn/VvZ7fmSdiObvnwuMG+irRpl5zi3DNgeEUcA/wWcP+z3\nYXMbUo6/APxpRBwGfBP486I43POu1wuA9flsw92Br0fEZ8hXQevBcmB/4P1kMxdfKOndEVHHgk/W\n3UJzDFlvbmad75uBvyk1QluoMnK8MSIemPkZeE/RE9zzrlcAp+RH7POAT/X15IgvRMQv589/HXCf\nC3fjLCjHuTvI1+cADgP+o5zQrCRl5Pizkn4j//kVwF1FT3DPu15nAtdIWgRMk510tHYpI8ergKsk\nnUm2vGmj714zgsrI8ZnAeyQ9Cnwf+OOiJ3htEzOzBHnYxMwsQS7eZmYJcvE2M0uQi7eZWYJcvM3M\nEuTibWaWIBdvM7ME/T8PXo+RxGsZ8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f596b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_mean.hist()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
